#!/usr/bin/env python3 import os import sys from optparse import OptionParser import numpy as np import damask scriptName = os.path.splitext(os.path.basename(__file__))[0] scriptID = ' '.join([scriptName,damask.version]) def operator(stretch,strain,eigenvalues): """Albrecht Bertram: Elasticity and Plasticity of Large Deformations An Introduction (3rd Edition, 2012), p. 102.""" return { 'V#ln': np.log(eigenvalues) , 'U#ln': np.log(eigenvalues) , 'V#Biot': ( np.ones(3,'d') - 1.0/eigenvalues ) , 'U#Biot': ( eigenvalues - np.ones(3,'d') ) , 'V#Green': ( np.ones(3,'d') - 1.0/eigenvalues/eigenvalues) *0.5, 'U#Green': ( eigenvalues*eigenvalues - np.ones(3,'d')) *0.5, }[stretch+'#'+strain] # -------------------------------------------------------------------- # MAIN # -------------------------------------------------------------------- parser = OptionParser(option_class=damask.extendableOption, usage='%prog options [ASCIItable(s)]', description = """ Add column(s) containing given strains based on given stretches of requested deformation gradient column(s). """, version = scriptID) parser.add_option('-u','--right', dest = 'right', action = 'store_true', help = 'material strains based on right Cauchy--Green deformation, i.e., C and U') parser.add_option('-v','--left', dest = 'left', action = 'store_true', help = 'spatial strains based on left Cauchy--Green deformation, i.e., B and V') parser.add_option('-0','--logarithmic', dest = 'logarithmic', action = 'store_true', help = 'calculate logarithmic strain tensor') parser.add_option('-1','--biot', dest = 'biot', action = 'store_true', help = 'calculate biot strain tensor') parser.add_option('-2','--green', dest = 'green', action = 'store_true', help = 'calculate green strain tensor') parser.add_option('-f','--defgrad', dest = 'defgrad', action = 'extend', metavar = '', help = 'heading(s) of columns containing deformation tensor values [%default]') parser.set_defaults( defgrad = ['f'], ) (options,filenames) = parser.parse_args() if len(options.defgrad) > 1: options.defgrad = options.defgrad[1:] stretches = [] strains = [] if options.right: stretches.append('U') if options.left: stretches.append('V') if options.logarithmic: strains.append('ln') if options.biot: strains.append('Biot') if options.green: strains.append('Green') if options.defgrad is None: parser.error('no data column specified.') # --- loop over input files ------------------------------------------------------------------------- if filenames == []: filenames = [None] for name in filenames: try: table = damask.ASCIItable(name = name, buffered = False) except IOError: continue damask.util.report(scriptName,name) # ------------------------------------------ read header ------------------------------------------ table.head_read() # ------------------------------------------ sanity checks ---------------------------------------- items = { 'tensor': {'dim': 9, 'shape': [3,3], 'labels':options.defgrad, 'column': []}, } errors = [] remarks = [] for type, data in items.items(): for what in data['labels']: dim = table.label_dimension(what) if dim != data['dim']: remarks.append('column {} is not a {}...'.format(what,type)) else: items[type]['column'].append(table.label_index(what)) for theStretch in stretches: for theStrain in strains: table.labels_append(['{}_{}({}){}'.format(i+1, # extend ASCII header with new labels theStrain, theStretch, what if what != 'f' else '') for i in range(9)]) if remarks != []: damask.util.croak(remarks) if errors != []: damask.util.croak(errors) table.close(dismiss = True) continue # ------------------------------------------ assemble header -------------------------------------- table.info_append(scriptID + '\t' + ' '.join(sys.argv[1:])) table.head_write() # ------------------------------------------ process data ------------------------------------------ stretch = {} outputAlive = True while outputAlive and table.data_read(): # read next data line of ASCII table for column in items['tensor']['column']: # loop over all requested defgrads F = np.array(list(map(float,table.data[column:column+items['tensor']['dim']])),'d').reshape(items['tensor']['shape']) (U,S,Vh) = np.linalg.svd(F) # singular value decomposition R_inv = np.dot(U,Vh).T # rotation of polar decomposition stretch['U'] = np.dot(R_inv,F) # F = RU stretch['V'] = np.dot(F,R_inv) # F = VR for theStretch in stretches: stretch[theStretch] = np.where(abs(stretch[theStretch]) < 1e-12, 0, stretch[theStretch]) # kill nasty noisy data (D,V) = np.linalg.eigh((stretch[theStretch]+stretch[theStretch].T)*0.5) # eigen decomposition (of symmetric(ed) matrix) neg = np.where(D < 0.0) # find negative eigenvalues ... D[neg] *= -1. # ... flip value ... V[:,neg] *= -1. # ... and vector for theStrain in strains: d = operator(theStretch,theStrain,D) # operate on eigenvalues of U or V eps = np.dot(V,np.dot(np.diag(d),V.T)).reshape(9) # build tensor back from eigenvalue/vector basis table.data_append(list(eps)) # ------------------------------------------ output result ----------------------------------------- outputAlive = table.data_write() # output processed line # ------------------------------------------ output finalization ----------------------------------- table.close() # close ASCII tables